Conventionally, Digital Elevation Model (DEM) data indicating digital elevation of each lattice-shaped region in a predetermined map region generated by using stereo pair images obtained from an aircraft, a satellite, and the like by using a remote sensing technology is provided. For example, DEM data generated from the Shuttle Radar Topography Mission (SRTM) and DEM data generated from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) are known.
Recently, topographical analyses are conducted by computers using these pieces of DEM data, and for example, an analysis of the geological structure based on a slope gradation map generated by using these pieces of DEM data has been proposed.
However, the DEM data generated by using the remote sensing technology contains noise components due to a fluctuation of an orbit of a satellite and lack of accuracy of stereo pair images. Further, when stereo pair images for generating the DEM data are acquired on different dates and times, atmospheric influences and the like cause differences between these images and noise components are contained therein. These noise components cause errors in the digital elevation at each point contained in the DEM data, and lead to a decrease in the accuracy of the analysis of the geological structure.
In Non-Patent Document 1, the inventors of the present invention proposed a method for removing noise components in SRTM DEM by conducting a process with a matrix filter for smoothing elevation data that constitutes a two-dimensional region in a map region by using the weighted moving average method or conducting a process with a line filter that filters data which is continuous in one direction in a map region.    Non-Patent Document 1: Makoto INOUE, Taro YAJIMA, “The filter effect on SRTM90mDEM—Advantages and Disadvantages—,” The Mining and Materials Processing Institute of Japan, Journal of the Spring Conference, 2011, (I) Resources, A12-1
When conducting high-accuracy analyses on the geological structures in a wide region, it is required to appropriately remove noise from many pieces of DEM data corresponding to each of these map regions. However, because trends of noise contained in these pieces of DEM data are different according to terrains indicated by the DEM data, atmospheric influences, and the like, a filter suitable for a certain piece of DEM data is not necessarily suitable for another piece of DEM data.